In 2012, the U.S. Department of Justice reported that 797,500 children younger than 18 went missing. On average, 2,185 children are reported missing each day. Unfortunately, children, or adults, that go missing are often found dead. When skeletal remains are found, the medical and legal communities typically rely on the skeletal remains to provide important facts about the individual. Often, the skull is sufficiently preserved to be used in forensic investigations. Forensic experts can provide a basic sketch of an unknown individual based on the skull, but such basic sketches are often time-consuming and too inaccurate to permit identification of the individual.
The technique of forensic facial reconstruction can greatly expedite otherwise lengthy identification investigations and serve to stimulate the memory of the public in order to identify unknown individuals. The aim is to obtain an approximate representation of the real face to suggest a resemblance to a missing person. The usefulness of this technique has been well documented in the study of war crime victims and in mass disasters worldwide.
Craniofacial identification has undergone significant technical maturation, beginning with two-dimensional (2D) and three-dimensional (3D) manual methods and more recent 2D and 3D computer assisted methods. The process began in 19th century Europe—artisans modeled clay in bulk over soft tissue depth markers placed at various locations on the skull, without much regard for the underlying anatomy. More recently in the United States, this method has been modified and standardized in an American method, which consists of building soft tissue layers in bulk, without consideration of the underlying anatomy, approximating tabulated tissue depths in key locations and interpolating between these landmarks. During the same period, other researchers developed a Russian method of craniofacial reconstruction that modeled the musculature of the face, muscle by muscle, onto the skull. The strategy behind this technique is that the placement of the muscles and soft tissues covered by a thin layer will lead to a more accurate representation. However, this method requires estimation of the points of muscle attachment, muscle thickness, and the appearance of the soft tissue layer covering the muscle. Further advances include efforts to include estimations for mouth width, eyeball projection, ear height, nose projection, pronasale position, superciliare position, lip closure line, and lip position. Despite this progress, current craniofacial identification methods have major limitations. Firstly, all methods are largely based on soft tissue depth prediction models, a process that has never been empirically tested. Secondly, facial approximation practitioners recognize that, with few exceptions, the location and size of the facial muscles cannot be accurately established. This is a consequence of muscles which originate and/or insert into the soft tissue alone, and do not interface directly with the skull, making accurate prediction unlikely. Thirdly, assessment methods to test the accuracy of facial reconstruction techniques are isolated and not well established. Accuracy is a challenging metric to assess since reconstructions need not closely resemble a suspect to be identified as that specific person. These limitations result in a current system which is technically sensitive, subjective, and reliant on artistic interpretation. Furthermore, since these facial reconstructions are costly and time-consuming, they are generally limited to a single reconstruction or not done at all. Collectively, these limitations restrict the power of current forensic facial reconstruction methods in investigations, leaving many cases unresolved.
With the exception of computerization of some methods, few changes have been introduced into the process of approximating a human face. Comprehensive reviews of these approaches have shown that the computerized systems virtually mimic manual methods of clay reconstruction, using digital tissue depth markers and algorithms to produce a smooth face-mesh over these markers. Some recent systems involve volume deformation models, which consist of soft tissue warping, where the face of an anthropologically similar individual (age, sex, race) is warped onto the matched soft-tissue markers of the unknown skull. Statistical and vector-based models have recently been proposed to mathematically reconstruct the most likely soft tissue match for a skull. A recent conceptual framework and review of computerized craniofacial reconstruction (FIG. 1) summarizes the technical steps of building a digital craniofacial model, beginning with selection of an appropriate template and consideration of bias in its selection, incorporation of explicit knowledge relating to facial surfaces, craniofacial deformation to geometrically align with the target skull, and finally registration of the model to the skull by a fitting/matching process. A concern with this multi-step approach is the variability, error, and subjectivity that occur at each step. At this time, mainstream digital approaches are not employed by forensic investigators. The inefficiency and inaccuracies inherent in current methods of facial approximation warrant the exploration of other estimation-based methods.
Accordingly, there is a need in the art for a faster, more accurate, and more objective system and method for approximating the soft tissue profile of a skull of an unknown individual.